101 to 125 of 269 Pandas Jobs

education to teams adopting AI tooling. What Youll Bring 5+ years in software/ML engineering, ideally with production deployment experience. Strong Python background, comfortable across data frameworks like Pandas, NumPy, SciPy, Dask, Polars, or PySpark. Proven experience setting up ML pipelines, integrating with AWS/Databricks, and applying CI/CD principles. Solid understanding of time-series forecasting and More ❯

to teams adopting AI tooling. What You’ll Bring • 5+ years in software/ML engineering, ideally with production deployment experience. • Strong Python background, comfortable across data frameworks like Pandas, NumPy, SciPy, Dask, Polars, or PySpark. • Proven experience setting up ML pipelines, integrating with AWS/Databricks, and applying CI/CD principles. • Solid understanding of time-series forecasting and More ❯

must have: 9+ years of hands-on Python experience (FastAPI, Flask, or Django) 5+ years of AWS experience (Lambda, API Gateway, Boto3, CloudFormation) Strong skills in REST APIs, SQL, Pandas, PyTest, and GitLab CI/CD Exposure to data pipelines, ETL, or event-driven systems Python, AWS Mirgation, and data manipulation will be required.Financial domain experience is a strong plus. More ❯

analyse and reverse engineer existing stored procedures to extract and understand business logic Federated Data Platform (FDP): Solid Python development experience for data engineering Excellent SQL skills Knowledge of Pandas and/or PySpark General Technical Skills: Git/GitHub and understanding of DevOps practices (CICD, secrets scanning) Experience with cloud-based engineering (Azure/AWS/GCP) Front-end More ❯

relevant insights Advanced statistical and analytical techniques and concepts sampling methods, Regression Properties of distributions Weighting sample-based data Statistical tests proper usage Real-world applications. Python - NumPy, SciPy, Pandas, MLlib, scikit-learn, and other common data and machine learning related libraries Working knowledge of SQL, data structures and databases (Snowflake - desirable) This is a pragmatic and humble organisation who More ❯

months with possible extensions (No Sponsorship Available ) Skills/Qualifications: · 4+ years of experience developing data pipelines and data warehousing solutions using Python and libraries such as Pandas, NumPy, PySpark, etc. · 3+ years hands-on experience with cloud services, especially Databricks, for building and managing scalable data pipelines · 3+ years of proficiency in working with Snowflake or similar cloud-based More ❯

AI-driven initiatives, including text and sentiment analysis Collaborating closely with Data Engineering and BI to deliver robust, end-to-end ML pipelines and model monitoring Tech Environment: Python (pandas, scikit-learn, modern ML frameworks) SQL for analysis and model input preparation Cloud environment (AWS/GCP/Azure) Exposure to NLP, MLOps, or CI/CD pipelines is a More ❯

and modelling skills, including time series and ensemble models. Experience in manipulating and interpreting data from disparate sources. Proficient in Python coding and core data science libraries, such as Pandas, TensorFlow, Scikit-learn etc. Ideally previous experience with Snowflake and DBT. Experience with Cloud Computing (AWS, GCP, Azure) - Ideally in AWS. Creativity and innovative thinking to explore diverse data sources More ❯

Technical & Analytical Skills Hands-on experience with large data sets and data profiling. Proficiency in data tools: Power BI, Excel, SQL, Python, Databricks. Familiarity with tools like Great Expectations, Pandas, Plotly, or MS Power Automate is a plus. Able to work collaboratively and independently in a fast-paced environment. Ready to Apply? If you're excited by the opportunity to More ❯

Technical & Analytical Skills Hands-on experience with large data sets and data profiling. Proficiency in data tools: Power BI, Excel, SQL, Python, Databricks. Familiarity with tools like Great Expectations, Pandas, Plotly, or MS Power Automate is a plus. Able to work collaboratively and independently in a fast-paced environment. Ready to Apply? If you're excited by the opportunity to More ❯

Master’s in Bioinformatics, Computer Science, Data Engineering, or related field. 4+ years of experience in data engineering or bioinformatics data management. Strong Python and SQL skills; experience with Pandas, PySpark, Dask, or similar frameworks. Familiar with Linux, Docker, and modern data architectures (relational, object, non-relational). Experience with orchestration tools (Airflow/Prefect) and cloud platforms (AWS preferred More ❯

Master’s in Bioinformatics, Computer Science, Data Engineering, or related field. 4+ years of experience in data engineering or bioinformatics data management. Strong Python and SQL skills; experience with Pandas, PySpark, Dask, or similar frameworks. Familiar with Linux, Docker, and modern data architectures (relational, object, non-relational). Experience with orchestration tools (Airflow/Prefect) and cloud platforms (AWS preferred More ❯

computer science, or a proven record of delivering quality software Curious, proactive, and motivated to write great code and improve as a developer Bonus Points For Experience With NumPy, Pandas HTML, CSS, JavaScript Git LaTeX Cybersecurity best practices Financial, legal, or compliance-related tech Location & Setup The engineering team is fully remote, with the existing developers based in South Africa. More ❯

trading constraints. Automate Data Pipelines, designing and managing workflows for collecting, cleaning, and storing large volumes of financial data (e.g., price, volume, fundamentals, alternative data), often using tools like Pandas, NumPy, and Dask. Collaborate Across Teams for Deployment, working with researchers, traders, and DevOps teams to integrate Python models into production environments (e.g., through APIs, microservices, or containerized systems like More ❯

predictive model- Clear explainability on what drives user clicks- A functional internal tool for non-technical stakeholders- Documentation and guidance for productionisation Required Skills Must-Have: Expert-level Python (Pandas, modelling libraries such as Scikit-learn) SQL AWS cloud experience Proven end-to-end ownership of data science solutions Model explainability (SHAP, LIME, feature importance) Ability to build simple dashboards More ❯

experiments into production-ready systems What You’ll Bring 6+ years of software engineering experience 4+ years building and deploying ML/AI models in production Proficiency in Python (pandas, scikit-learn, PyTorch, etc.) Experience with modern backend stacks (TypeScript, Node.js, Go, or similar) Strong understanding of ML principles and model evaluation Experience with cloud-based model deployment (GCP preferred More ❯

experiments into production-ready systems What You’ll Bring 6+ years of software engineering experience 4+ years building and deploying ML/AI models in production Proficiency in Python (pandas, scikit-learn, PyTorch, etc.) Experience with modern backend stacks (TypeScript, Node.js, Go, or similar) Strong understanding of ML principles and model evaluation Experience with cloud-based model deployment (GCP preferred More ❯

the latest developments in AI, machine learning, and data science methodologies. Experienced Needed: Masters or PhD in a STEM subject Proficiency in Python, with experience in libraries such as pandas, scikit-learn, TensorFlow, or PyTorch. Solid SQL skills and experience working with relational databases. Exposure to cloud platforms (AWS, GCP, or Azure) would be advantageous. Strong analytical and problem-solving More ❯

the latest developments in AI, machine learning, and data science methodologies. Experienced Needed: Masters or PhD in a STEM subject Proficiency in Python, with experience in libraries such as pandas, scikit-learn, TensorFlow, or PyTorch. Solid SQL skills and experience working with relational databases. Exposure to cloud platforms (AWS, GCP, or Azure) would be advantageous. Strong analytical and problem-solving More ❯

a related discipline Demonstrated project experience (academic research, dissertation work, or personal projects) applying machine learning or AI techniques Strong programming skills in Python (e.g. PyTorch, TensorFlow, scikit-learn, pandas, NumPy) Familiarity with cloud platforms (AWS, Azure, or GCP) and basic distributed systems concepts Strong problem-solving mindset with a passion for building practical, scalable AI solutions Excellent teamwork and More ❯

a related discipline Demonstrated project experience (academic research, dissertation work, or personal projects) applying machine learning or AI techniques Strong programming skills in Python (e.g. PyTorch, TensorFlow, scikit-learn, pandas, NumPy) Familiarity with cloud platforms (AWS, Azure, or GCP) and basic distributed systems concepts Strong problem-solving mindset with a passion for building practical, scalable AI solutions Excellent teamwork and More ❯

driven setting? Were looking for a talented OPS Engineer to join our growing teams in Erskine or Newcastle. Key Responsibilities Strong proficiency in Python and ML libraries such as: pandas, NumPy, scikit-learn XGBoost, LightGBM, CatBoost TensorFlow, Keras, PyTorch Experience with model deployment and serving tools: ONNX, TensorRT, TensorFlow Serving, TorchServe Familiarity with ML lifecycle tools: MLflow, Kubeflow, Azure ML More ❯

technical and non-technical stakeholders. Stay ahead of the curve by experimenting with emerging AI and ML techniques. What You’ll Bring Strong background in Python or R (e.g., Pandas, NumPy, Scikit-learn, TensorFlow). Skilled in SQL and statistical analysis. Proven experience in data science, ideally within energy, optimisation, or IoT-focused applications. Solid grasp of machine learning techniques More ❯

production-ready features Contributing to both R&D-heavy and commercially driven engineering initiatives What you can bring Strong Python skills, including experience with data processing libraries (e.g. Numpy, Pandas). Bonus: RasterIO or other geospatial tooling experience. Experience with AWS and containerised environments (Docker). Familiarity with backend frameworks (FastAPI, Flask, or Django). Experience working with data pipelines More ❯

production-ready features Contributing to both R&D-heavy and commercially driven engineering initiatives What you can bring Strong Python skills, including experience with data processing libraries (e.g. Numpy, Pandas). Bonus: RasterIO or other geospatial tooling experience. Experience with AWS and containerised environments (Docker). Familiarity with backend frameworks (FastAPI, Flask, or Django). Experience working with data pipelines More ❯